TY - JOUR
T1 - Integrating transcriptomics, metabolomics, and GWAS helps reveal molecular mechanisms for metabolite levels and disease risk
AU - FinnGen
AU - Yin, Xianyong
AU - Bose, Debraj
AU - Kwon, Annie
AU - Hanks, Sarah C.
AU - Jackson, Anne U.
AU - Stringham, Heather M.
AU - Welch, Ryan
AU - Oravilahti, Anniina
AU - Fernandes Silva, Lilian
AU - Locke, Adam
AU - Fuchsberger, Christian
AU - Service, Susan K.
AU - Erdos, Michael R.
AU - Bonnycastle, Lori L.
AU - Kuusisto, Johanna
AU - Stitziel, Nathan O.
AU - Hall, Ira
AU - Morrison, Jean
AU - Ripatti, Samuli
AU - Palotie, Aarno
AU - Freimer, Nelson B.
AU - Collins, Francis S.
AU - Mohlke, Karen L.
AU - Scott, Laura J.
AU - Fauman, Eric B.
AU - Burant, Charles
AU - Boehnke, Michael
AU - Laakso, Markku
AU - Wen, Xiaoquan
N1 - Publisher Copyright:
© 2022 The Author(s)
PY - 2022/10/6
Y1 - 2022/10/6
N2 - Transcriptomics data have been integrated with genome-wide association studies (GWASs) to help understand disease/trait molecular mechanisms. The utility of metabolomics, integrated with transcriptomics and disease GWASs, to understand molecular mechanisms for metabolite levels or diseases has not been thoroughly evaluated. We performed probabilistic transcriptome-wide association and locus-level colocalization analyses to integrate transcriptomics results for 49 tissues in 706 individuals from the GTEx project, metabolomics results for 1,391 plasma metabolites in 6,136 Finnish men from the METSIM study, and GWAS results for 2,861 disease traits in 260,405 Finnish individuals from the FinnGen study. We found that genetic variants that regulate metabolite levels were more likely to influence gene expression and disease risk compared to the ones that do not. Integrating transcriptomics with metabolomics results prioritized 397 genes for 521 metabolites, including 496 previously identified gene-metabolite pairs with strong functional connections and suggested 33.3% of such gene-metabolite pairs shared the same causal variants with genetic associations of gene expression. Integrating transcriptomics and metabolomics individually with FinnGen GWAS results identified 1,597 genes for 790 disease traits. Integrating transcriptomics and metabolomics jointly with FinnGen GWAS results helped pinpoint metabolic pathways from genes to diseases. We identified putative causal effects of UGT1A1/UGT1A4 expression on gallbladder disorders through regulating plasma (E,E)-bilirubin levels, of SLC22A5 expression on nasal polyps and plasma carnitine levels through distinct pathways, and of LIPC expression on age-related macular degeneration through glycerophospholipid metabolic pathways. Our study highlights the power of integrating multiple sets of molecular traits and GWAS results to deepen understanding of disease pathophysiology.
AB - Transcriptomics data have been integrated with genome-wide association studies (GWASs) to help understand disease/trait molecular mechanisms. The utility of metabolomics, integrated with transcriptomics and disease GWASs, to understand molecular mechanisms for metabolite levels or diseases has not been thoroughly evaluated. We performed probabilistic transcriptome-wide association and locus-level colocalization analyses to integrate transcriptomics results for 49 tissues in 706 individuals from the GTEx project, metabolomics results for 1,391 plasma metabolites in 6,136 Finnish men from the METSIM study, and GWAS results for 2,861 disease traits in 260,405 Finnish individuals from the FinnGen study. We found that genetic variants that regulate metabolite levels were more likely to influence gene expression and disease risk compared to the ones that do not. Integrating transcriptomics with metabolomics results prioritized 397 genes for 521 metabolites, including 496 previously identified gene-metabolite pairs with strong functional connections and suggested 33.3% of such gene-metabolite pairs shared the same causal variants with genetic associations of gene expression. Integrating transcriptomics and metabolomics individually with FinnGen GWAS results identified 1,597 genes for 790 disease traits. Integrating transcriptomics and metabolomics jointly with FinnGen GWAS results helped pinpoint metabolic pathways from genes to diseases. We identified putative causal effects of UGT1A1/UGT1A4 expression on gallbladder disorders through regulating plasma (E,E)-bilirubin levels, of SLC22A5 expression on nasal polyps and plasma carnitine levels through distinct pathways, and of LIPC expression on age-related macular degeneration through glycerophospholipid metabolic pathways. Our study highlights the power of integrating multiple sets of molecular traits and GWAS results to deepen understanding of disease pathophysiology.
KW - colocalizataion
KW - genome-wide association study
KW - metabolomics
KW - transcriptome-wide association study
KW - transcriptomics
UR - http://www.scopus.com/inward/record.url?scp=85139335907&partnerID=8YFLogxK
U2 - 10.1016/j.ajhg.2022.08.007
DO - 10.1016/j.ajhg.2022.08.007
M3 - Article
C2 - 36055244
AN - SCOPUS:85139335907
SN - 0002-9297
VL - 109
SP - 1727
EP - 1741
JO - American journal of human genetics
JF - American journal of human genetics
IS - 10
ER -